Proposal for ASME DSCC/MOVIC 2012 Special Session on

The Future of Bicycle and Motorcycle Dynamics and Control

Session Organizers

Stephen M. Cain, Andrew Dressel, Mont Hubbard, Jason K. Moore, Arend L. Schwab

Bicycle and motorcycle dynamics research has seen a revival of sorts in the past decade. For example, in 2010 the first Bicycle and Motorcycle Dynamics conference was held in Delft, Netherlands where many of this proposed sessions participants begun closer interaction and collaboration. Small, robust sensor and microcontroller technologies paired with the complex and mostly unstable dynamics of single track vehicles lend the vehicles to be excellent platforms for dynamics and control studies with and without the human rider in the loop. While a good deal of progress has been made on understanding the open-loop dynamics of these systems, there is still much research to be done to understand how to provide stability, control, and desirable handling for a human rider and on our ability to exploit advanced control to effectively automate the vehicle in both balance and tracking. This session will highlight some of the latest research trends in the field from robotic bicycles and motorcycle simulators to tire property measurement and identification of the human controller and close with a roundtable discussion on the future of the research.

The six abstracts below represent research from around the globe that is helping the small but growing single track vehicle community make significant strides towards developing a better understanding of the systems. Each presenter will give a brief “lightning” talk to highlight their work and the final half of the session will be an open ended discussion moderated by the chair focused on the direction of future research in the field. The presentations and discussions will provide a view of the forefront of single track vehicle research and the questions that are driving the research from all corners of the globe.

We have a total of six presenters in this proposed frontier research forum.  They are:

  1. Andrew Dressel, “Comparing the Predictions of Rotta’s Tire Model to Data Collected from Bicycle Tires”
  2. Ichiro Kageyama, Yukiyo Kuriyagawa, and Yuya Aikawa, “Research on modeling of two-wheeled vehicle behavior by influence of sidewall rigidity of tire”
  3. Dale L. Peterson and Mont Hubbard, “Robotic bicycle control and system identification”
  4. Jason K. Moore, Mont Hubbard, and Ronald Hess, “System identification of a bicycle under manual control”
  5. M. Massaro, V. Cossalter, R. Lot, R. Sartori, S. Rota, and M. Ferrari, “A mobile driving simulator for single-track vehicles
  6. Junghsen Lieh, “Closed form and numerical solutions of traction in cycling”

Comparing the Predictions of Rotta’s Tire Model to Data Collected from Bicycle Tires

Andrew Dressel

University of Wisconsin-Milwaukee

Rotta’s tire model [1] predicts a tire’s contact patch size and shape based solely on the tire’s dimensions, the inflation pressure, and the vertical load applied. The model also predicts the static lateral force generated for a given lateral displacement. Developed for aircraft tires, it depends upon the inflated tire assuming the shape of a torus, and should be well suited to road bicycle tires. We use the model to predict these values for such tires and compare them to empirical data collected directly from physical examples.

We also measured cornering stiffness and camber thrust directly for several bicycle tires under various  loads and inflation pressures and present these results.

[1] Rotta, J., (1949 ). “Zur Statik des Luftreifens (Statics of a pneumatic tire).” Ingenieur Archiv, Vol 17

Research on modeling of two-wheeled vehicle behavior by influence of sidewall rigidity of tire.

Ichiro Kageyama, Yukiyo Kuriyagawa, and Yuya Aikawa

Nihon University

This paper deals with two wheeled vehicle behavior by influence of lateral and torsional rigidity of tire side wall. The characteristic of a theoretic model and an experimental result, A wobble mode, which is one of important vibration modes, cannot fully be expressed using ordinary model for two-wheeled vehicle dynamics. Undesirable vibration, which appears in the handle system called the shimmy, usually appears around 80 km/h, and it disappears beyond this speed. Normally, the model for two-wheeled vehicle dynamics, however, cannot describe such characteristics of vibration.  The influence on sidewall rigidity of front tire to the steering system was checked experimentally, and it was found that the frequency of the system was close to that of the shimmy.

Therefore, we took into consideration the model of sidewall rigidity of the tire to the total model.  Therefore, we took into consideration the model of sidewall rigidity of the tire to two-wheeled vehicle model. As a result, it was shown that the model has good description.

Robotic bicycle control and system identification

Dale L. Peterson and Mont Hubbard

University of California, Davis

A bicycle equipped with sensors and actuators has been constructed for the purpose of performing system identification experiments.  Sensors include optical steer and wheel angle sensors as well as 3-axis rate gyroscopes and accelerometers fixed to the rear bicycle frame.  Actuators include a rear wheel hub motor and a direct drive steer motor.  Sensor data collection, data logging and motor commands are managed by a 32-bit ARM Cortex-M3 processor in which the state estimation and feedback control laws are programmed.  Yaw rate, a natural variable to command during steady turns, was chosen as the tracked reference signal so that minor trim adjustments to the heading of the bicycle during experiments could be performed and avoid crashes.

Measurement of the bicycle's physical parameters (mass, inertia, and geometry) was performed to calculate model parameters that most closely describe the experimental apparatus. The dynamic model is functionally equivalent to the Whipple model, with the addition of toroidal tires. Direct measurement of steer angle is used in conjunction with this dynamic bicycle model to construct a full state estimate which is used in a yaw rate feedback controller. The bicycle is equipped with a 2.4GHz ISM band radio; sensor measurements and computed control command are sent to a PC to enable realtime monitoring; a basic set of commands can be sent from the PC to the bicycle to start and stop the controller and apply different disturbance steer torques for system identification experiments.

This paper discusses the design considerations of the controller/observer, simulation results, and preliminary system identification results.

System identification of a bicycle under manual control and lateral perturbations

Jason K. Moore, Mont Hubbard, and Ronald Hess

University of California, Davis

In this study, we empirically derive a simple fourth order dynamic model of a bicycle. This model is then compared to various basic bicycle models derived from first principles. The empirical model was developed by using a combination of state space system identification techniques and linear regression on a large set of experimental data. The data was collected during a series of experiments on a gymnasium floor and a large treadmill. During the experiments, three rigidified bicycle riders manually controlled the bicycle using only steering control for several tasks: simple balancing, line tracking, and both tasks with measured lateral pulsive perturbations. The bicycle was instrumented to accurately measure the rider’s applied steering torque, all of the essential kinematics of the bicycle’s motion, and the lateral perturbation force. The resulting the rider control actions excited the system in a bandwidth adequate for identifying the linear dynamics of the bicycle with standard state space system identification techniques. A weighted linear regression was used to empirically derive relationships for the state and input matrices from the identified data as a function of speed. The resulting models were compared with several archetypal models using a custom interactive program allowing the comparison of statistically derived models to the first principles models, showing that the simplest bicycle models are not necessarily sufficient for capturing the fundamental dynamics of the vehicle. The roll acceleration was found to be reasonably predicted by the simpler first principles bicycle models, but the steer acceleration was not. A model which includes the inertial effects of the rider’s arms, which more closely modeled reality, improved the steer acceleration and steer torque predictions, but still exhibited some deficiencies. We conclude the paper by exploring the validity of several of the modeling assumptions and potential sources for the error, with a particular focus on the tire to floor interaction.

A mobile driving simulator for single-track vehicles

M. Massaro, V. Cossalter, R. Lot, R. Sartori, S. Rota, and M. Ferrari

University of Padua, Department of Industrial Engineering

While hundreds of car driving simulators are illustrated in the open scientific literature, only a limited number of driving simulators for Powered-Two-Wheelers (PTW) exist, [1]. In recent years a number of research projects have been promoted to resolve this gap and improve road safety through the introduction of Intelligence Transportation Systems (ITS) developed on PTW simulators, see e.g. [2],[3],[4]. To this extend the simulators are essential for human and hardware in the loop simulations during the design and test stages of ITS.

In 2011 a new mobile driving simulator for powered two wheelers vehicle has been designed and built (Figure 1) at the University of Padova for several purposes. The simulator is not only to develop and test electronic devices such as antilock braking systems and traction control systems, but also to investigate different design choices on PTW dynamics, to train drivers, and to study their behaviors in different scenarios. Thus the mobile requirement is essential to easily use the simulator in safety events and different driving schools (the simulator easily fits into a van when disassembled in three main parts). The simulator consists of a scooter mock-up, actuators for the mock-up motion, sensors to monitor rider’s actions, three wide screens to represent the scenario from the rider point of view and a software for the real-time simulation of the vehicle dynamics.

This paper presents the architecture and the peculiarities of the vehicle and tire models employed, which have been developed based on the advanced model reported in [5] and optimized for real-time simulations. Several examples of telemetry logged from the simulator are presented to highlight how the simulator captures the most important PTW dynamics, such as the counter-steering maneuver to start a cornering maneuver, the over-steering behavior when excessive throttle is used to exit a curve and the coupling between longitudinal and lateral forces takes place, the weave instability when the vehicle is fit with the wrong tire set, etc.

The simulator has been recently exposed at the European Researchers’ Night, Palazzo Bo’, Padova (IT), 23 September 2011, where several drivers were allowed to test it.

Figure 1. The mobile simulator

[1] Nehaoua L., H. Arioui, S. Mammar, “Review on single track vehicle and motorcycle simulators”, 19th Mediterranean Conference on Control and Automation, Aquis Corfu Holiday Palace, Corfu, Greece, June 20-23, 2011.

[2] Huth, V., Biral, F., Martín, O. & Lot, R., “Comparison of two warning concepts of an intelligent Curve Warning system for motorcyclists in a simulator study“, Accident Analysis and Prevention, Jan;44(1):118-25. 2012.

[3] Bekiaris E. D., Spadoni A., Nikolaou S. I., “SAFERIDER Project: new safety and comfort in Powered Two Wheelers”, 2009 2nd Conference on Human System Interactions, HSI '09 , art. no. 5091045 , pp. 600-602.

[4] Cossalter, V., Lot, R., Massaro, M., Sartori, R., 2011, “Development and validation of an advanced motorcycle riding simulator”, Proc of the IMechE, Part D: Journal of Automobile Engineering, 225(6), pp. 705-720.

[5] Cossalter, V., Lot, R. and Massaro, M., 2011, “An advanced multibody code for handling and stability analysis of motorcycles”, Meccanica, 46(5), pp. 943-958.

Closed form and numerical solutions of traction in cycling

Junghsen Lieh, PhD

Professor, Mechanical & Materials Engineering

Wright State University, Dayton, Ohio 45435

In theory, how fast can a cyclist pedal and how much power is required from him? Traditionally, pure numerical methods have been used to answer these questions. To improve this, a closed-form method is developed and used to solve for the speed and power requirement during cycling and its results are compared with those obtained from numerical methods. The simulation is conducted in Matlab/Simulink and used to study the effect of air drag coefficient, frontal area, rolling resistance and road gradient on the speed and power consumption.